A New Multi-objective Approach for Molecular Docking Based on RMSD and Binding Energy

  • Esteban López-CamachoEmail author
  • María Jesús García-Godoy
  • José García-Nieto
  • Antonio J. Nebro
  • José F. Aldana-Montes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9702)


Ligand-protein docking is an optimization problem based on predicting the position of a ligand with the lowest binding energy in the active site of the receptor. Molecular docking problems are traditionally tackled with single-objective, as well as with multi-objective approaches, to minimize the binding energy. In this paper, we propose a novel multi-objective formulation that considers: the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands and the binding (intermolecular) energy, as two objectives to evaluate the quality of the ligand-protein interactions. To determine the kind of Pareto front approximations that can be obtained, we have selected a set of representative multi-objective algorithms such as NSGA-II, SMPSO, GDE3, and MOEA/D. Their performances have been assessed by applying two main quality indicators intended to measure convergence and diversity of the fronts. In addition, a comparison with LGA, a reference single-objective evolutionary algorithm for molecular docking (AutoDock) is carried out. In general, SMPSO shows the best overall results in terms of energy and RMSD (value lower than 2Å for successful docking results). This new multi-objective approach shows an improvement over the ligand-protein docking predictions that could be promising in in silico docking studies to select new anticancer compounds for therapeutic targets that are multidrug resistant.


Molecular docking Multi-objective optimization Nature inspired metaheuristics Algorithm comparison 



This work is partially funded by Grants TIN2011-25840 (Ministerio de Ciencia e Innovación) and P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz de Investigación, Desarrollo e Innovación). This article is based upon work from COST Action CA15140, supported by COST (European Cooperation in Science and Technology).


  1. 1.
    Boisson, J.C., Jourdan, L., Talbi, E., Horvath, D.: Parallel multi-objective algorithms for the molecular docking problem. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 187–194, September 2008Google Scholar
  2. 2.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  3. 3.
    Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)zbMATHGoogle Scholar
  4. 4.
    García-Godoy, M.J., López-Camacho, E., García-Nieto, J., Nebro, A.J., Aldana-Montes, J.F.: Solving molecular docking problems with multi-objective metaheuristics. Molecules 20(6), 10154–10183 (2015)CrossRefGoogle Scholar
  5. 5.
    Grosdidier, A., Zoete, V., Michielin, O.: EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins 67(4), 1010–1025 (2007)CrossRefGoogle Scholar
  6. 6.
    Gu, J., Yang, X., Kang, L., Wu, J., Wang, X.: MoDock: a multi-objective strategy improves the accuracy for molecular docking. Algorithms Mol. Biol. 10, 8 (2015)CrossRefGoogle Scholar
  7. 7.
    Janson, S., Merkle, D., Middendorf, M.: Molecular docking with multi-objective particle swarm optimization. Appl. Soft Comput. 8(1), 666–675 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kukkonen, S., Lampinen, J.: GDE3: the third evolution step of generalized differential evolution. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 443–450 (2005)Google Scholar
  9. 9.
    Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 229–242 (2009)CrossRefGoogle Scholar
  10. 10.
    López-Camacho, E., García-Godoy, M.J., Nebro, A.J., Aldana-Montes, J.F.: jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework. Bioinformatics 30(3), 437–438 (2014)CrossRefGoogle Scholar
  11. 11.
    López-Camacho, E., García-Godoy, M.J., García-Nieto, J., Nebro, A.J., Aldana-Montes, J.F.: Solving molecular flexible docking problems with metaheuristics: a comparative study. Appl. Soft Comput. 28, 379–393 (2015)CrossRefGoogle Scholar
  12. 12.
    Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., Olson, A.J.: AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30(16), 2785–2791 (2009)CrossRefGoogle Scholar
  13. 13.
    Nebro, A.J., Durillo, J.J., Garcia-Nieto, J., Coello Coello, C.A., Luna, F., Alba, E.: SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In: IEEE Symposium on Computational Intelligence in Multi-criteria Decision-Making, pp. 66–73, March 2009Google Scholar
  14. 14.
    Norgan, A.P., Coffman, P.K., Kocher, J.P.A., Katzmann, D.J., Sosa, C.P.: Multilevel parallelization of AutoDock 4.2. J. Cheminform. 3(1), 12 (2011)CrossRefGoogle Scholar
  15. 15.
    Oduguwa, A., Tiwari, A., Fiorentino, S., Roy, R.: Multi-objective optimisation of the protein-ligand docking problem in drug discovery. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1793–1800 (2006)Google Scholar
  16. 16.
    Sandoval-Perez, A., Becerra, D., Vanegas, D., Restrepo-Montoya, D., Nino, F.: A multi-objective optimization energy approach to predict the ligand conformation in a docking process. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 181–192. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  17. 17.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, New York (2007)zbMATHGoogle Scholar
  18. 18.
    Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Esteban López-Camacho
    • 1
    Email author
  • María Jesús García-Godoy
    • 1
  • José García-Nieto
    • 1
  • Antonio J. Nebro
    • 1
  • José F. Aldana-Montes
    • 1
  1. 1.Khaos Research Group, Department of Computer ScienceUniversity of Málaga, ETSI InformáticaMálagaSpain

Personalised recommendations